{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 监督学习算法:  k近邻(knn),线性回归,朴素贝叶斯,决策树,支持向量机, 神经网络,逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#导入相关模块\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sc\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import mglearn\n",
    "%matplotlib inline\n",
    "\n",
    "#忽略弹出的warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')  \n",
    "pd.set_option('display.float_format', lambda x: '%.5f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.k近邻"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 判断肿瘤是良性人体肿瘤还是恶性肿瘤,benign表示良性,malignant表示恶性肿瘤"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(569, 30)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99000</td>\n",
       "      <td>10.38000</td>\n",
       "      <td>122.80000</td>\n",
       "      <td>1001.00000</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.24190</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38000</td>\n",
       "      <td>17.33000</td>\n",
       "      <td>184.60000</td>\n",
       "      <td>2019.00000</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.71190</td>\n",
       "      <td>0.26540</td>\n",
       "      <td>0.46010</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57000</td>\n",
       "      <td>17.77000</td>\n",
       "      <td>132.90000</td>\n",
       "      <td>1326.00000</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.18120</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99000</td>\n",
       "      <td>23.41000</td>\n",
       "      <td>158.80000</td>\n",
       "      <td>1956.00000</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.24160</td>\n",
       "      <td>0.18600</td>\n",
       "      <td>0.27500</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0     17.99000      10.38000       122.80000 1001.00000          0.11840   \n",
       "1     20.57000      17.77000       132.90000 1326.00000          0.08474   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760         0.30010              0.14710        0.24190   \n",
       "1           0.07864         0.08690              0.07017        0.18120   \n",
       "\n",
       "   mean fractal dimension           ...             worst radius  \\\n",
       "0                 0.07871           ...                 25.38000   \n",
       "1                 0.05667           ...                 24.99000   \n",
       "\n",
       "   worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       "0       17.33000        184.60000  2019.00000           0.16220   \n",
       "1       23.41000        158.80000  1956.00000           0.12380   \n",
       "\n",
       "   worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       "0            0.66560          0.71190               0.26540         0.46010   \n",
       "1            0.18660          0.24160               0.18600         0.27500   \n",
       "\n",
       "   worst fractal dimension  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据肿瘤读取\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "cancer = load_breast_cancer()\n",
    "X = pd.DataFrame(cancer.data,columns=cancer.feature_names)\n",
    "print(X.shape)\n",
    "X.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['malignant' 'benign']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target\n",
       "0       0\n",
       "1       0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#标签数据,malignant表示恶性, benign表示良性\n",
    "y = pd.DataFrame(cancer.target,columns=[\"target\"])\n",
    "print(cancer.target_names)\n",
    "y.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    357\n",
       "0    212\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#标签数据分布,较均衡, 无需处理\n",
    "y[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.09706398, -2.07333501,  1.26993369, ...,  2.29607613,\n",
       "         2.75062224,  1.93701461],\n",
       "       [ 1.82982061, -0.35363241,  1.68595471, ...,  1.0870843 ,\n",
       "        -0.24388967,  0.28118999],\n",
       "       [ 1.57988811,  0.45618695,  1.56650313, ...,  1.95500035,\n",
       "         1.152255  ,  0.20139121],\n",
       "       ..., \n",
       "       [ 0.70228425,  2.0455738 ,  0.67267578, ...,  0.41406869,\n",
       "        -1.10454895, -0.31840916],\n",
       "       [ 1.83834103,  2.33645719,  1.98252415, ...,  2.28998549,\n",
       "         1.91908301,  2.21963528],\n",
       "       [-1.80840125,  1.22179204, -1.81438851, ..., -1.74506282,\n",
       "        -0.04813821, -0.75120669]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据标准化\n",
    "#X = X.apply(lambda x: (x-x.mean())/x.std()) \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler_X = scaler.fit(X).transform(X)\n",
    "scaler_X #查看经标准化后的数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#数据集切分\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11b7c0550>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IX7gYAKB76Tkozv8tcUVEREREdLMYqCVWMnEyisffA5mpELrHHgFKSqQuiYiIiIhuAgO1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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b5b6160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#精度\n",
    "#训练集精度\n",
    "training_accuracy=[]\n",
    "#测试集精度\n",
    "test_accuracy=[]\n",
    "###############################################################################\n",
    "#n_nigighbors取值从1到10\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neighbors_settings = range(1,11)\n",
    "for n_neighbors in neighbors_settings:\n",
    "    #模型训练\n",
    "    clf = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    clf.fit(X_train,y_train)\n",
    "    #记录训练集精度\n",
    "    training_accuracy.append(clf.score(X_train,y_train))\n",
    "    #记录泛化精度\n",
    "    test_accuracy.append(clf.score(X_test,y_test))\n",
    "    \n",
    "##############################################################################\n",
    "#模型可视化\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.plot(neighbors_settings,training_accuracy,label=\"training accurary\",lw=2,color=\"r\")\n",
    "plt.plot(neighbors_settings,test_accuracy,label=\"test accurary\",lw=2,color=\"g\")\n",
    "plt.xlabel(\"Accurary\")\n",
    "plt.ylabel(\"n_neighbors\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.线性回归---岭回归模型和Lasso回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 均方误差(mean squared error)是预测值与真实值之差除以样本数\n",
    "##### 正则化是指对模型做显示约束(通过alpha参数控制),避免过拟合, 岭回归正是利用L2正则化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 岭回归模型是通过控制系数防止过拟合, Lasso是通过筛选变量防止过拟合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 岭回归---预测波士顿房价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11fa2df28>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qlFavXq3nnntOCxYsaPXaGRlJcjjsVnysiLjdKVG7Ns4e6tzzUePYQJ1jA3WO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\nDADnAC8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dc37a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#预测波士顿房价\n",
    "#导入数据\n",
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()\n",
    "X = pd.DataFrame(boston.data,columns=boston.feature_names)\n",
    "y = pd.DataFrame(boston.target,columns=[\"target\"])\n",
    "#数据集切分\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=0)\n",
    "#构建岭回归模型\n",
    "train_accurary=[]\n",
    "test_accurary=[]\n",
    "a_alpha = [0.00001,0.005,0.01,0.05,0.1,0.5,1,5,10,100] \n",
    "for a in a_alpha:\n",
    "    from sklearn.linear_model import Ridge\n",
    "    ridge = Ridge(alpha=a).fit(x_train,y_train) #通过调节alpha参数做显示约束\n",
    "    train_accurary.append(ridge.score(x_train,y_train))\n",
    "    test_accurary.append(ridge.score(x_test,y_test))\n",
    "    \n",
    "##############################################################################\n",
    "#精度可视化\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.plot(a_alpha,train_accurary,label=\"train accurary\",lw=2,color=\"r\")\n",
    "plt.plot(a_alpha,test_accurary,label=\"test accurary\",lw=2,color=\"g\")\n",
    "plt.xlabel(\"Accurary\")\n",
    "plt.ylabel(\"a_alpha\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### lasso 回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11e9356d8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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994u76FzU8o9YLKT36kvcxm1kNmmGJS6OkIH9CenWCcs5LSESERFxNyrUuXDL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\nVprWctKKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cda7b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#预测波士顿房价\n",
    "#导入数据\n",
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()\n",
    "X = pd.DataFrame(boston.data,columns=boston.feature_names)\n",
    "y = pd.DataFrame(boston.target,columns=[\"target\"])\n",
    "#数据集切分\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=0)\n",
    "#构建岭回归模型\n",
    "train_accurary=[]\n",
    "test_accurary=[]\n",
    "a_alpha = [0.0001,0.005,0.01,0.05,0.1,0.5,1,5,10] \n",
    "for a in a_alpha:\n",
    "    from sklearn.linear_model import Lasso\n",
    "    lasso = Lasso(alpha=a).fit(x_train,y_train) #通过调节alpha参数做显示约束\n",
    "    train_accurary.append(lasso.score(x_train,y_train))\n",
    "    test_accurary.append(lasso.score(x_test,y_test))\n",
    "    \n",
    "##############################################################################\n",
    "#精度可视化\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.plot(a_alpha,train_accurary,label=\"train accurary\",lw=2,color=\"r\")\n",
    "plt.plot(a_alpha,test_accurary,label=\"test accurary\",lw=2,color=\"g\")\n",
    "plt.xlabel(\"Accurary\")\n",
    "plt.ylabel(\"a_alpha\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.逻辑回归(logistics)and线性支持向量机(线性SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11b7e70b8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3KEKFRXFxicaNGxfnbEVRuADpW3xQWABAf52dnWpuDm1i7y1IWlpa+s02jx8/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\nqfx/def8kaxAR40AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11df66320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression #导入逻辑回归模型\n",
    "from sklearn.svm import LinearSVC #导入线性支持向量机模型\n",
    "\n",
    "X, y = mglearn.datasets.make_forge() #导入数据\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
    "\n",
    "for model, ax in zip([LinearSVC(), LogisticRegression()], axes):\n",
    "    clf = model.fit(X, y) #训练模型\n",
    "    mglearn.plots.plot_2d_separator(clf, X, fill=False, eps=0.5,\n",
    "                                    ax=ax, alpha=.7) #可视化模型结果\n",
    "    mglearn.discrete_scatter(X[:, 0], X[:, 1], y, ax=ax)\n",
    "    ax.set_title(\"{}\".format(clf.__class__.__name__))\n",
    "    ax.set_xlabel(\"Feature 0\")\n",
    "    ax.set_ylabel(\"Feature 1\")\n",
    "axes[0].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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51S8l86q5glUrV5ryak7r2XqlOylLC5llXm3DzFonOLgt1qz5HB999B80bRqA+fPnYPjw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\ng5+bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b541da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对于逻辑回归和线性支持向量机,决定正则化强度的权衡参数C,C值越大, 对正则化越弱.\n",
    "#不同参数C下的线性SVM在数据集上的决策边界\n",
    "mglearn.plots.plot_linear_svc_regularization()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 用于多分类的线性SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11d89cc50>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eqM7OJeYpgPUj+m2ZO3C9NF/wM0KtdSVWqvPG1QIFQ71OnTrw9/e3fB8QEFBj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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e9e1748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据并对数据分布可视化\n",
    "from sklearn.datasets import make_blobs\n",
    "X, y = make_blobs(random_state=42)\n",
    "plt.figure(figsize=(8,6))\n",
    "mglearn.discrete_scatter(X[:, 0], X[:, 1], y)\n",
    "plt.xlabel(\"Feature 0\")\n",
    "plt.ylabel(\"Feature 1\")\n",
    "plt.legend([\"Class 0\", \"Class 1\", \"Class 2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#构建模型\n",
    "from sklearn.svm import LinearSVC #导入模型\n",
    "line_svm = LinearSVC(penalty=\"l2\",C=0.1)\n",
    "liner_svm = line_svm.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11bf0a908>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mv2K9Ppd4cz9SDKPpXtZDUs6F39NlL0NlMGB4/LcQrPHosR1lNXXvHsF997WR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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bf920b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#模型可视化\n",
    "mglearn.discrete_scatter(X[:, 0], X[:, 1], y)\n",
    "line = np.linspace(-15, 15)\n",
    "for coef, intercept, color in zip(liner_svm.coef_, liner_svm.intercept_,\n",
    "                                  mglearn.cm3.colors):\n",
    "    plt.plot(line, -(line * coef[0] + intercept) / coef[1], c=color)\n",
    "plt.ylim(-10, 15)\n",
    "plt.xlim(-10, 8)\n",
    "plt.xlabel(\"Feature 0\")\n",
    "plt.ylabel(\"Feature 1\")\n",
    "plt.legend(['Class 0', 'Class 1', 'Class 2', 'Line class 0', 'Line class 1','Line class 2'], loc=(1.01, 0.3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.朴素贝叶斯分类器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### scikit-learn中实现三种朴素贝叶斯分类器: GaussianNB,BernoulliNB和MultionmialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征统计:\n",
      "{0: array([0, 1, 0, 2]), 1: array([2, 0, 2, 1])}\n"
     ]
    }
   ],
   "source": [
    "X = np.array([[0, 1, 0, 1],\n",
    "              [1, 0, 1, 1],\n",
    "              [0, 0, 0, 1],\n",
    "              [1, 0, 1, 0]])\n",
    "y = np.array([0, 1, 0, 1])\n",
    "\n",
    "counts = {}\n",
    "for label in np.unique(y):\n",
    "    # 对每个类别进行遍历\n",
    "    # 统计每个特征中出现1的个数\n",
    "    counts[label] = X[y == label].sum(axis=0)\n",
    "print(\"特征统计:\\n{}\".format(counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集准确度: 0.977\n",
      "测试集准确度: 0.944\n"
     ]
    }
   ],
   "source": [
    "#决策树\n",
    "from sklearn.tree import DecisionTreeClassifier #导入决策树模型\n",
    "from sklearn.datasets import load_breast_cancer #导入数据\n",
    "\n",
    "#肿瘤数据读取\n",
    "cancer = load_breast_cancer() \n",
    "\n",
    "#数据集切分\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    cancer.data, cancer.target, stratify=cancer.target, random_state=42)\n",
    "#构建模型\n",
    "tree = DecisionTreeClassifier(max_depth=3,random_state=0) #构建模型,参数max_depth是调节模型深度\n",
    "tree.fit(X_train, y_train) #模型训练\n",
    "print(\"训练集准确度: {:.3f}\".format(tree.score(X_train, y_train)))\n",
    "print(\"测试集准确度: {:.3f}\".format(tree.score(X_test, y_test)))\n",
    "#发生了过拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0NHDDDTfw3HPPERUVxYsvvoivry/vvPMO27ZtY+rUqcyYMYMjR44AkJaWRmBgYPtabNmy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\nRJxCBVNERERERESc4v8AiRT2pNNo3O8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bfab748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#决策树特征重要性\n",
    "def plot_feature_importances_cancer(model):\n",
    "    n_features = cancer.data.shape[1]\n",
    "    plt.figure(figsize=(14,8))\n",
    "    plt.barh(range(n_features), model.feature_importances_, align='center')\n",
    "    plt.yticks(np.arange(n_features), cancer.feature_names)\n",
    "    plt.xlabel(\"Feature importance\")\n",
    "    plt.ylabel(\"Feature\")\n",
    "    plt.ylim(-1, n_features)\n",
    "\n",
    "plot_feature_importances_cancer(tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11fa4feb8>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ico1xJ8hp1/xpHs2f5lF3b5/2FdVr+8Fq7S2q14vvlejF90qUFBemhVnxWjQ9XomxYWZH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\niIiDqLkWEREREXGQ/w+YHZ8x7Yk9wQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11faa24a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#预测计算机内存价格\n",
    "import os\n",
    "ram_prices = pd.read_csv(\"ram_price.csv\")\n",
    "plt.figure(figsize=(12,7))\n",
    "plt.semilogy(ram_prices.date, ram_prices.price)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Price in $/Mbyte\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# 利用历史数据预测2000年后的计算机内存价格\n",
    "data_train = ram_prices[ram_prices.date < 2000]\n",
    "data_test = ram_prices[ram_prices.date >= 2000]\n",
    "\n",
    "# 基于日期来预测价格\n",
    "X_train = data_train.date[:, np.newaxis]\n",
    "# 利用对数变换得到数据和目标之间更简单的关系\n",
    "y_train = np.log(data_train.price)\n",
    "\n",
    "tree = DecisionTreeRegressor().fit(X_train, y_train)\n",
    "linear_reg = LinearRegression().fit(X_train, y_train)\n",
    "\n",
    "# 对所有数据进行预测\n",
    "X_all = ram_prices.date[:, np.newaxis]\n",
    "\n",
    "pred_tree = tree.predict(X_all)\n",
    "pred_lr = linear_reg.predict(X_all)\n",
    "\n",
    "# 对数变换逆运算\n",
    "price_tree = np.exp(pred_tree)\n",
    "price_lr = np.exp(pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11bfa1400>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TNqDR7SwWC21/NJ2qv71KzdLPqLmxL54+Xk6uVkRERKRpZrOZX/ziCZ588hc4HA68vX14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\nGpoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114852160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#模型可视化\n",
    "plt.figure(figsize=(12,10))\n",
    "plt.semilogy(data_train.date, data_train.price, label=\"Training data\")\n",
    "plt.semilogy(data_test.date, data_test.price, label=\"Test data\")\n",
    "plt.semilogy(ram_prices.date, price_tree, label=\"Tree prediction\")\n",
    "plt.semilogy(ram_prices.date, price_lr, label=\"Linear prediction\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x128f82f98>,\n",
       " <matplotlib.lines.Line2D at 0x128f8cc50>]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bPf3HyQfi8uNv6Os4OpWVsWRlHBFXxlKKiCg3/3+0VqnEf3vmVKpfc+/3n5V/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\nt3zP4QNThZ4X8qTZKpK6+mo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GFMVesxHffPlK5ufXkiRWFpdjZWl5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\nF0o7cwT6JTf1rwpZqlPR/Wh22k4apZ6JV0Wy05uq0M+7kZ2gN9mJTmS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kzi2pc9zeYmiewEpBUDTAlWXVRxqbTW1Oux32225e2cRoqDNZe7B7VaytLGOACAtCE7IUpkJwCA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\n97eDpzV+0dbs9O3xo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0TE21feO1lV8sbxysnXgfxkpzT8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\ntk6zzHcXHsatfTf6WaU+AFykNjIqslN6njbyHXShPgBlIjsxKqlnp3v3e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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11de45978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier #随机森林模型\n",
    "from sklearn.datasets import make_moons\n",
    "#数据集读取及切分\n",
    "X, y = make_moons()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y,random_state=42)\n",
    "#构建模型\n",
    "forest = RandomForestClassifier(n_estimators=11, random_state=2) #参数n_estimators控制森林的颗树\n",
    "forest.fit(X_train, y_train)\n",
    "#可视化\n",
    "fig, axes = plt.subplots(4, 3, figsize=(30, 20))\n",
    "for i, (ax, tree) in enumerate(zip(axes.ravel(), forest.estimators_)):\n",
    "    ax.set_title(\"Tree {}\".format(i))\n",
    "    mglearn.plots.plot_tree_partition(X_train, y_train, tree, ax=ax)\n",
    "    \n",
    "mglearn.plots.plot_2d_separator(forest, X_train, fill=True, ax=axes[-1, -1],alpha=.4)\n",
    "axes[-1, -1].set_title(\"Random Forest\")\n",
    "mglearn.discrete_scatter(X_train[:, 0], X_train[:, 1], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ad3d3+vfv3/rz2bNnARg6dChOTk707t0bd3d3Tpw4Qa9evUhOTub666+noqKC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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124d13eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#特征权重\n",
    "from sklearn.ensemble import RandomForestClassifier #随机森林模型\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "#数据集读取及切分\n",
    "cancer = load_breast_cancer()\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target,random_state=0)\n",
    "#构建模型\n",
    "forest = RandomForestClassifier(n_estimators=100, random_state=2) #参数n_estimators控制森林的颗树\n",
    "forest.fit(X_train, y_train)\n",
    "\n",
    "plot_feature_importances_cancer(forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 支持向量机"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1291c6dd8>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f9+UHSDgNIDwEk4VTV51VcyPLZporny94Wo9nZMeNcLJ74DWKiHVjXpUXBMF8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WSZ6kGaUJVqQc8lVPmt/Y1YmDLNLFuROBRr7y/ll2Qs9gaZbYymx7owNlj6jL3iHaKl9GmFsn/Z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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1291b2828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#导入数据\n",
    "from sklearn.datasets import make_blobs\n",
    "X,y = make_blobs(centers=4,random_state=8)\n",
    "y=y%2\n",
    "# 增加新特征\n",
    "X_new = np.hstack([X, X[:, 1:] ** 2])\n",
    "\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D, axes3d\n",
    "figure = plt.figure()\n",
    "# 三维数据可视化\n",
    "ax = Axes3D(figure, elev=-152, azim=-26)\n",
    "#先画出所有y==0的点,然后画出所有y==1的点\n",
    "mask = y == 0\n",
    "ax.scatter(X_new[mask, 0], X_new[mask, 1], X_new[mask, 2], c='b',\n",
    "           cmap=mglearn.cm2, s=60, edgecolor='k')\n",
    "ax.scatter(X_new[~mask, 0], X_new[~mask, 1], X_new[~mask, 2], c='r', marker='^',\n",
    "           cmap=mglearn.cm2, s=60, edgecolor='k')\n",
    "ax.set_xlabel(\"feature0\")\n",
    "ax.set_ylabel(\"feature1\")\n",
    "ax.set_zlabel(\"feature1 ** 2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1290026d8>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ih05DNAaZ0ALh1h5hDRPqXq5Shv0i+/SahEPCiYjEKJ1yUWpu9IgUXSNUq9UHP2CEdcYV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\nub974xtwza/+GvY/87LW13741a/gyh/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HHNmIPynJdJSctSmKWtLushzhudgR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7AAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124cb3f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#构建模型\n",
    "from sklearn.svm import LinearSVC\n",
    "linear_svm_3d = LinearSVC().fit(X_new, y)\n",
    "\n",
    "coef, intercept = linear_svm_3d.coef_.ravel(), linear_svm_3d.intercept_\n",
    "\n",
    "# 显示香型决策边界\n",
    "figure = plt.figure()\n",
    "ax = Axes3D(figure, elev=-152, azim=-26)\n",
    "xx = np.linspace(X_new[:, 0].min() - 2, X_new[:, 0].max() + 2, 50)\n",
    "yy = np.linspace(X_new[:, 1].min() - 2, X_new[:, 1].max() + 2, 50)\n",
    "\n",
    "XX, YY = np.meshgrid(xx, yy)\n",
    "ZZ = (coef[0] * XX + coef[1] * YY + intercept) / -coef[2]\n",
    "ax.plot_surface(XX, YY, ZZ, rstride=8, cstride=8, alpha=0.3)\n",
    "ax.scatter(X_new[mask, 0], X_new[mask, 1], X_new[mask, 2], c='b',\n",
    "           cmap=mglearn.cm2, s=60, edgecolor='k')\n",
    "ax.scatter(X_new[~mask, 0], X_new[~mask, 1], X_new[~mask, 2], c='r', marker='^',\n",
    "           cmap=mglearn.cm2, s=60, edgecolor='k')\n",
    "\n",
    "ax.set_xlabel(\"feature0\")\n",
    "ax.set_ylabel(\"feature1\")\n",
    "ax.set_zlabel(\"feature1 ** 2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x124c98400>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nDHG457aJXqKZHgDCwvcgX758uWpra/XSSy8pEolo/fr1fhepZFgFvCTLKXIS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d89cdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ZZ = YY ** 2\n",
    "dec = linear_svm_3d.decision_function(np.c_[XX.ravel(), YY.ravel(), ZZ.ravel()])\n",
    "plt.contourf(XX, YY, dec.reshape(XX.shape), levels=[dec.min(), 0, dec.max()],\n",
    "             cmap=mglearn.cm2, alpha=0.5)\n",
    "mglearn.discrete_scatter(X[:, 0], X[:, 1], y)\n",
    "plt.xlabel(\"Feature 0\")\n",
    "plt.ylabel(\"Feature 1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.神经网络(深度学习)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11d074358>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ovRQI9vdEgK8HFDIJvD3k8FIp4OUhh6dKDi9V2WdPlQweysrPKmVZcdtza9Yd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d06e5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#模型原型\n",
    "line = np.linspace(-3, 3, 100)\n",
    "plt.plot(line, np.tanh(line), label=\"tanh\")\n",
    "plt.plot(line, np.maximum(line, 0), label=\"relu\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"relu(x), tanh(x)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x129500e80>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6KYlScpSR0SKU3GYAZLWwQaVytxNkJTuzcHiDQIvQqdWjK+ZktVFBPy+c3ARR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\ng9k8MwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129408278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier #导入神经网络模块\n",
    "from sklearn.datasets import make_moons #导入数据\n",
    "\n",
    "X, y = make_moons(n_samples=100, noise=0.25, random_state=3) #数据读取\n",
    "#数据切分\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y,\n",
    "                                                    random_state=42)\n",
    "#构建神经网络模型\n",
    "mlp = MLPClassifier(solver='lbfgs', random_state=0).fit(X_train, y_train) #默认一个隐含层100个隐节点\n",
    "#模型可视化\n",
    "mglearn.plots.plot_2d_separator(mlp, X_train, fill=True, alpha=.3)\n",
    "mglearn.discrete_scatter(X_train[:, 0], X_train[:, 1], y_train)\n",
    "plt.xlabel(\"Feature 0\")\n",
    "plt.ylabel(\"Feature 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12974ed30>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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esmlfAcygokUoFb9oTSkz7zlTylDVRlCYps8LlQrTf8se1KBOSmIqOcqoaBFK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\ngEEELQAABv1/TbF/eWpVYZ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1295d5860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlp = MLPClassifier(solver='lbfgs', random_state=0, hidden_layer_sizes=[100,100,100,100]) #使用4个隐层100个隐含点\n",
    "mlp.fit(X_train, y_train)\n",
    "mglearn.plots.plot_2d_separator(mlp, X_train, fill=True, alpha=.3)\n",
    "mglearn.discrete_scatter(X_train[:, 0], X_train[:, 1], y_train)\n",
    "plt.xlabel(\"Feature 0\")\n",
    "plt.ylabel(\"Feature 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JvE5G+SzgerelQRh5bXp7BxuZktexFoFN6R/TTHB56bNi4kVPrUbY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\n+ETFkgc308WiDowfi+mMEJXyURpJ2lM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RoNPIujB7fe6q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      "text/plain": [
       "<matplotlib.figure.Figure at 0x129291128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#不同隐藏层不同隐点对比\n",
    "fig, axes = plt.subplots(2, 4, figsize=(25, 10))\n",
    "for axx, n_hidden_nodes in zip(axes, [10, 100]):#隐藏层\n",
    "    for ax, alpha in zip(axx, [0.0001, 0.01, 0.1, 1]):#alpha表示学习率\n",
    "        mlp = MLPClassifier(solver='lbfgs', random_state=0,\n",
    "                            hidden_layer_sizes=[n_hidden_nodes, n_hidden_nodes],\n",
    "                            alpha=alpha)\n",
    "        mlp.fit(X_train, y_train)\n",
    "        mglearn.plots.plot_2d_separator(mlp, X_train, fill=True, alpha=.3, ax=ax)\n",
    "        mglearn.discrete_scatter(X_train[:, 0], X_train[:, 1], y_train, ax=ax)\n",
    "        ax.set_title(\"n_hidden=[{}, {}]\\nalpha={:.4f}\".format(\n",
    "                      n_hidden_nodes, n_hidden_nodes, alpha))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. 总结\n",
    "#### knn模型适用于小型数据,是很好的基准模型, 很好解释\n",
    "#### 线性模型适用于大型数据,也适用于高维数据\n",
    "#### 朴素贝叶斯只适用于分类问题, 使用大数据和高维数据,精度比线性模型低,但速度快\n",
    "#### 决策树速度快,且不需要缩放数据, 可以可视化, 很容易理解\n",
    "#### 随机森林几乎总比单颗树好, 但不适用高纬稀疏数据,不好解释\n",
    "#### 支持向量机, 需要数据缩放,对参数敏感\n",
    "#### 神经网络 特别适用于复杂的模型, 需要数据缩放, 对参数选取敏感"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
